Visualizing Items and Measures: An Overview and Demonstration of the Kernel Smoothing Item Response Theory Technique

Abstract

The current demonstration was conducted to familiarize a broader audience of applied researchers in psychology and social sciences with the benefits of an exploratory psychometric technique -- kernel smoothing item response theory (KSIRT). A data-driven, nonparametric KSIRT provides a visual representation of the characteristics of the items in a measure (scale or test) and offers convenient preliminary feedback about the functioning of the items and the measure in a particular research context. The technique could be a useful addition to the analytical toolkit of applied researchers that work with a range of measures, within the classical test theory or IRT framework. KSIRT is described and its use is demonstrated with a set of items from a psychological well-being measure. A recently developed, easy to use R package was utilized to perform the analyses and the R code is included in the manuscript.

Keywords

• exploratory psychometric analysis
• irt
• kernel smoothing
• nonparametric regression
• visualization
• r